Spatial Behavioral Data: Collection and Use in Activity Scheduling Models
Bibliographic record
Abstract
Transport models and their practical applications are becoming increasingly more sophisticated, thus requiring more precise input data, including spatial data. The development, testing, and assessment of a survey method with which to collect multiday information on activity–travel patterns with a high level of spatial detail and accuracy are described. The survey consists of a computer-assisted, self-completing weekly household activity-travel diary survey program combined with an interactive map for spatial data input and visualization. Compared with traditional paper-and-pencil surveys, more accurate spatial data is gathered through this survey, especially for activity locations, routes, and trip lengths, which a first trial on a small sample of 30 households clearly demonstrated. In addition, more details on activity and travel times were collected. Fatigue effects did not become evident, and the respondent burden was considered to be acceptable by the participants. Additional testing of the respondents’ map-handling abilities showed that people have different map-orientation aptitudes that depend on sociodemographic characteristics such as gender, income, and education. This suggests that maps incorporated in travel surveys should be adapted in different ways to accommodate different levels of map handling. Future household travel surveys, especially emerging computerized and Global Positioning System-supported methods, would appear to benefit from the integration of a spatial interface both to add detail and to reduce respondent burden. This is particularly important for activity-based or activity-scheduling surveys, which increasingly attempt to obtain higher amounts of precise and detailed information on individual behavior and choices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".